Ligand Based Pharmacophore Modelling, Virtual Screening And Molecular Docking Of Novel Compounds Against Diabetes

Abstract

Diabetes Mellitus is also called Type 2 diabetes, a disorder related to metabolism considered by insulin resistance, high sugar level in blood and lack of insulin production in body. It primarily occurs as a result of obesity, sedentary lifestyle, stress, nutrition and toxins.CAPN10, which encodes the cysteine protease calpain 10, was the first type 2 diabetes mellitus (T2DM) susceptibility gene identified through a genome-wide scan followed by positional cloning. A haplotype combination comprising three intronic CAPN10 single-nucleotide polymorphisms (UCSNP-43,-19, and-63) was associated with increased risk of T2DM in the population in which linkage was first found. In this study an attempt was madeto design a novel antidiabetic compound. A total of 21 existing compounds were taken from which 6 compounds were set as test and remaining 15 as training. Pharmacophore models were generated and their shared pharmacophore feature was identified. Virtual screening was performed and hit compounds were selected as inhibitor compounds. The generated shared feature pharmacophore showed 2 main features as 2 hydrogen bond acceptors and 1 aromatic ring. After the complete analysis, pharmacophores of all compounds were matched and novel pharmacophore was identified. Virtual screening was performed against the shared feature pharmacophoreto identify hit compounds 3 hit compounds were retrieved and docked.

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BEPLS Vol 8 [9] August 2019 38 | P a g e ©2019 AELS, INDIA
Bulletin of Environment, Pharmacology and Life Sciences
Bull. Env. Pharmacol. Life Sci., Vol 8 [9] August 2019 : 38-48
©2019 Academy for Environment and Life Sciences, India
Online ISSN 2277-1808
Journal’s URL:http://www.bepls.com
CODEN: BEPLAD
Global Impact Factor 0.876
Universal Impact Factor 0.9804
NAAS Rating 4.95
ORIGINAL ARTICLE OPEN ACCESS
Ligand Based Pharmacophore Modelling, Virtual Screening And
Molecular Docking Of Novel Compounds Against Diabetes
Fariha Maryam,Hana Mukhtar*, Iqra Bibi,Muhammad Rizwan, Sajid Khan, Azhar Mehmood, Anum
Munir
Department of Bioinformatics, Government Post Graduate College Mandian Abbottabad
E-mail: hanamukhtar06@gmail.com
ABSTRACT
Diabetes Mellitus is also called Type 2 diabetes, a disorder related to metabolism considered by insulin resistance, high
sugar level in blood and lack of insulin production in body. It primarily occurs as a result of obesity, sedentary lifestyle,
stress, nutrition and toxins.CAPN10, which encodes the cysteine protease calpain 10, was the first type 2 diabetes mellitus
(T2DM) susceptibility gene identified through a genome-wide scan followed by positional cloning. A haplotype
combination comprising three intronic CAPN10 single-nucleotide polymorphisms (UCSNP-43, -19, and -63) was
associated with increased risk of T2DM in the population in which linkage was first found. In this study an attempt was
madeto design a novel antidiabetic compound. A total of 21 existing compounds were taken from which 6 compounds
were set as test and remaining 15 as training. Pharmacophore models were generated and their shared pharmacophore
feature was identified. Virtual screening was performed and hit compounds were selected as inhibitor compounds. The
generated shared feature pharmacophore showed 2 main features as 2 hydrogen bond acceptors and 1 aromatic ring.
After the complete analysis, pharmacophores of all compounds were matched and novel pharmacophore was identified.
Virtual screening was performed against the shared feature pharmacophoreto identify hit compounds 3 hit compounds
were retrieved and docked.
Keywords: Diabetes Mellitus, CAPN10, Pharmacophore Modelling, Ligand, Molecular Docking, Virtual Screening.
Received 11.05.2019 Revised 23.06.2019 Accepted 15.07. 2019
INTRODUCTION
Diabetes mellitus (DM) is one of the first diseases occurring in human. It had been first identified in
Egyptians3000 years back,when interaction between hereditary components and environmental
factors[1]. DM was first described as metabolic syndrome component in 1988. It is also known as non-
insulin dependent characterized by the lack of insulin, insulin conflict and hyperglycemia. Environmental,
hereditary and behavioral factors are also responsible for the development of disease. The people having
diabetes mellitus are more in danger to different kinds of complications that frequently causes their early
death. It is a long-lasting metabolic disease increasing day by day around the world[2],[3].
There are two types of diabetes established by Hinsworth in 1935. Both forms of diabetes are described
by constant increase of sugar level in plasma[1], type 1 diabetes has been known as insulin dependent
and is an autoimmune disease caused when the B-cells in pancreatic islets are completely lost resulting in
insulin deficiency, this type can be treated with insulin injections[4], on the other hand T2D is caused
when the insulin secreted by islets cannot circulate properly in the tissues where it is required e.g. in
liver, muscles and fats. It is also called Insulin non-dependent and is most common in adults. It can be
treated by hypoglycemic drugs and diet control[5].
There are number of genes contributing to diabetes mellitus, calpain-10 gene (CAPN10) is one of the
proteases which serves as intracellular calcium-dependent cysteine proteases. Secretion of insulin and its
metabolism is regulated by CAPN10 protein. It is the first gene to be recognized through a genome scan
and positional cloning involved in DM, in which the expression of CAPN10 is changed with
polymorphisms. The mRNA of CAPN10 is mostly expressed in the heart, kidney, liver,pancreas and brain.
It is present on chromosome 2q37.3 and it is the first candidate gene for diabetes mellitus. Different tests

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are required to diagnose Diabetes Mellitus i-e glycated hemoglobin (A1C) test, random blood sugar test,
fasting blood sugar test and oral glucose tolerance test[6],[1].
Two therapeutic approaches have been developed to this problem; (GLP-1 analoguesimproving half-life
and DPP IV inhibitor stops the breakdown of endogenous GLP-1) these agents are used to control the
fasting and postprandial glucose level and in improved functioning of beta cells[7]. For the designing of
Ligand based drugs for DM molecular docking methods has been commonly used. [7],[12].
Pharmacophore modelling is greatly being used in drug discovery. In 1967 the conception of
Pharmacophore modelling has been given by Kier and according to him Pharmacophore modelling “is the
arrangement of functional feature that a compound or drug must have for desired expression” and the
term Pharmacophore was coined by Ehlrich[13].. They are the collections of atoms in 3D with functional
groups which shows interaction with receptors. Pharmacophore modelling is divided into two types:
structure based pharmacophore modelling and ligand based pharmacophore modelling[14],[15].
This study intends to identify novel compounds for DM using Ligand based pharmacophore modelling
approach.
MATERIAL AND METHODS
Figure 1 show the methods which were used to conduct this research.
Figure 1: Flowchart of methodology applied for the pharmacophore modelling procedure
Selection of CAPN10 Protein and existing Drugs:
From literature review CAPN10 gene was selected[1] after screening and the mutated structure with 1
mutation was downloaded with protein ID 3BOW from RCSB PDB after applying some filters. PDB(PDB;
http://www.rcsb.org/pdb/) a useful database that contains the 3D structures of nucleic acids or proteins
basically by using techniques like NMR and X-Ray Crystallography[16]. The existing compounds for
Diabetes were also screened and the 3D structures of these compounds were downloaded from PubChem.
Pubchem (https://pubchem.ncbi.nlm.nih.gov) a freely accessible chemistry database that contain
chemical molecules along with their activities against biological assays[17].
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Protein Preparation:
The protein with Id name 3BOW is the 3D crystal structure of CAPN10 gene in complex with
Calpastatin.The structures were then imported to Ligand Scout(www.inteligand.com/ligandscout) a
software that allows creating 3D pharmacophore models[18]. After importing the structure the energy of
these compounds is minimized and are then further used for pharmacophore generation.
Figure 2: 3D structure of 3BOW
Generation of Pharmacophore:
For every compound the pharmacophore is generated because it is the first important step to understand
the relationship between ligand and receptor. From all the pharmacophores generated a shared feature
pharmacophore was generated which shows and gives all the common and shared features of all the
generated pharmacophores
Virtual Screening of hit compounds against shared feature pharmacophore:
Then performed screening of shared feature pharmacophore with the zincdb.ldb library and 76
compounds were obtained by performing screening that had 90% similarity with these compounds. By
using the known pharmacophore validation set of antidiabetic drugs which are available in the market
the models of pharmacophore were tested.
Use of Lipinski’s rule for Validation of Hit compounds:
In order to estimate the drugs likeliness property, Lipinski's rule (rule of five) is used, it is an important
and standard rule to evaluate the properties of drugs and define the toxicity of these compounds by the
Protox server and only three compounds were obtained after screening that were fulfilling Lipinski
rule[19]. These three compounds were downloaded. The rules of five are:The hydrogen bond donors
should not be more than 5, M. Weight should not exceed 500Da, Log P should be less than 5 (or MLogP is
over 4.15) and the hydrogen bond acceptor should not be more than 10.
Docking of CAPN10 Protein with Hit compounds:
The Hit compounds which were fulfilling the Lipinski rule of 5 were downloaded and docked with
mutated CAPN10 protein for validation by using PatchDock server. And these were compared and
analyzed by using Discovery Studio.
RESULTS:
The protein sequence of 3BOW has 993 residues of amino acid and a molecular weight 114049.26
Daltons.It is vital in Pharmacophore modelling to select the test set and training set compounds[15].For
the discovery of novels compounds two types of pharmacophore modelling is used. First one is ligand
based and the second one is structure based. Here we used the ligand based pharmacophores for the
discovery of novel antidiabetic compounds. Ligand based pharmacophore models are significant for those
proteins whose 3D structures are still not predicted[20].
The activity and the properties of not only test compounds but also training compounds can be identified
through a good pharmacophore model.The 21 existing antidiabetic compounds were used as the test ant
training set. The compounds for test set were selected on the fact that these compounds are active in
many models of animals and are mostly used in the treatment of Diabetes. Among these 21 compounds,
six were set as test and 15 were set as training.
Table 1 shows the structure and ID’s of the training set compounds along with their names which were
not being used actively for the treatment or these compounds were used in combinations with other
drugs.
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Table 1: Training set and their chemical structures
S.no
ID NO
COMPOUND NAME
CHEMICAL STRUCTURE
1
56843247
SYNJARDY
2
54592203
FURAN THIAZOLIDINEDIONES,A47
3
44814423
ERTUGLIFLOZIN
4
24812958
CANAGLIFLOZIN
5
11243969
SAXAGLIPTIN
6
10096344
LINAGLIPTIN
7
9887712
DAPAGLIFLOZIN
8
5311309
NATEGLINIDE
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9
3033769
RACLOPRIDE
10
441314
MIGLITOL
11
77999
ROSIGLITAZONE
12
65981
REPAGLINIDE
13
41774
ACARBOSE
14
5503
TALAZAMIDE
15
11450633
ALOGLIPTIN BENZOATE
Table 2 shows the structure and ID’s of the test set compounds along with their names which were being
actively used for the treatment.
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Table 2: Test set and their chemical structures
S.no
ID NO
COMPOUND NAME
CHEMICAL STRUCTURES
1
10198397
2,4 Thiazolidinedione
2
4369359
SITAGLIPTIN
3
4829
PIOGLITAZONE
4
4091
METFORMIN
5
3478
GLIPIZIDE
6
3476
GLIMEPIRIDE
Here we used the ligand based pharmacophores for the discovery of novel antidiabetic compounds.For
this study Ligand Scout (www.inteligand.com/ligandscout) was used to generate the pharmacophore
models for the test and training set. Before pharmacophore generation minimization of energy of ligand is
important. The pharmacophore for each compound has been generated using “default settings” by
clicking on “create pharmacophore” command in menu.
Figure 3 shows the five main Pharmacophoric features for 25 ligands: Hydrogen bond acceptor (HBA),
Hydrogen bond donor (HBD), Hydrophobic region (H), Aromatic rings (AR) and ionizable positive regions
(PI). In each pharmacophore model of the compounds the red arrows represent HBA, green arrow
symbolizes HBD, a yellow sphere donates AR.
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Figure 3: Pharmacophores of all the compounds
A shared feature pharmacophore model was developed on the basis of common Pharmacophoric features
present in all pharmacophore models and comprises two HBD’s and one AR. The shared pharmacophore
model is shown in Figure 4.
Figure 4: Shared feature Pharmacophore showing Hydrogen bond Acceptors (Red spheres) and Aromatic
ring (Yellow Sphere)
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The screening was performed against shared pharmacophore model and total 76 Hit compounds were
obtained that shown 90% similarity. Lipinski rule was applied on these 76 hit compounds for validation
purpose. Only three compounds were fulfilling the Lipinski rule and their toxicity values and LD50 values
were also checked shown in Table 3.
Table 3: Toxicity class, LD50 value, structure and Zinc ID’s for Hit compounds fulfilling Lipinski’s rule.
S.no
Hit compound Zinc ID
Toxicity Class
LD50 Value
Structure
1
Zinc_8442109
4
325mg/kg
2
Zinc_8442186
4
730mg/kg
3
Zinc_8442268
4
650mg/kg
The ligands were docked in the active pocket of the mutated CAPN10 protein for further validation.
Docking is a powerful tool and the main purpose of docking is to bind the ligand with 3D structure of
protein. The docking score with the types and distance of bonds were measured. The interaction of amino
acids residues and high docking score show that this attempt of designing a novel compound against DM
is successful. The docking results of ligands and CAPN10 protein are shown in Figure 5, 6 and 7.
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Figure 5 shows the docking result of Zinc_8442109 with mutated CAPN10 protein, with binding score of
5340 and binding energy of -190.33 in this docked complex the common interactive amino acids
identified were MET, ILE, LEU and VAL. Figure 6 shows the docking result of Zinc_8442186 with mutated
CAPN10 protein, with binding score of 5268 and binding energy of -132.85 in this docked complex the
common interactive amino acids identified were TYR, LYS, MET, HIS, GLN and CYS. And Figure 7 shows
the docking result of Zinc_8442268 with mutated CAPN10 protein, with binding score of 5450 and
binding energy of -260.27 in this docked complex the common interactive amino acids identified were
GLU, MET, LEU, LYS, and PRO
DISCUSSION
Pharmacophore models are useful for designing lead structure and identified also for Binding explaining
site. Ligand-based methods use only ligand information for predicting activity depending on its
similarity/dissimilarity to previously known active ligands. It relies on knowledge of other molecules that
bind to the biological target of interest, which may be used to derive a pharmacophore model that will
define the minimum necessary structural characteristics a molecule must possess to bind to the target. It
relies on knowledge of other molecules that bind to the biological target of interest, which may be used to
derive a pharmacophore model that will define the minimum necessary structural characteristics a
molecule must possess to bind to the target.[20],[21]
2,4 Thiazolidinedione, Acarbose, Actoplus, Albiglutide, Alogliptin Benzoate and Metformin are currently
being used as treatment regimens. Several side effects headache. digestive discomfort, fatigue,
hypoglycemia, death and liver cell injury have been reported[8],[9]. In curing diabetes, the main task is to
normalize the sugar level of blood. For maximum control of glycaemia these drugs or therapies are used
Figure 6: Actively docked conformation of
second Zinc compound into CAPN10’s cavity
Figure 5: Actively docked conformation of First Zinc
compound into CAPN10’s cavity
Figure 7: Actively docked conformation of third Zinc
compound into CAPN10’s cavity
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individually or in combination with other drugs, but these drugs have some limitations as they are
expensive with some side effects, their pharmacokinetics properties and also their success rate is very
low[10],[11]. So the search for new drugs or class of compounds is on-going which would have less side
effects and more success rate than existing drugs.
In this present work, the pharmacophores were generated from the 21 antidiabetic compounds and same
technique of identification and pharmacophore generation has been reported in many researches. The
selection of compounds for dataset is the first and the most crucial step in pharmacophore model
generation. The pharmacophore was generate by using Ligand Scout which showed two main features as
Aromatic rings which are identified in Yellow color and Hydrogen bond Acceptors which are identified in
Red color. All the ligands showed uniformity in these two features. The pharmacophores of all
compounds are shown in Figure 2.
The similar features of all compounds were identified by generating their Shared feature Pharmacophore.
Screening of shared featured pharmacophore was done with zinc libraries that helped to discover hit
compounds and then on these hit compounds Lipinski rule of 5 was applied. The compounds for further
validation were docked with the receptor protein. The actively docked conformation of zinc compounds
into the binding cavity of CAPN10 gene and the strong binding interaction of ligand andCAPN10 showed
the validation of pharmacophore model shown in Figure 3, 4 and 5.
CONCLUSION
The present work was done to determine the novel compounds against Diabetes Mellitus and an inhibitor
for CAPN10 gene which successfully binds to its active site and helps to resist this disease. These novel
compounds were obtained by using the strategies like ligand based pharmacophore modelling, Validation
by docking and Virtual screening. The newly generated pharmacophore had shown two main common
features i-e Hydrogen bond acceptors and Aromatic rings.Compounds show least variations from each
other structurally but have the similar mode of action. Treatment choices for Diabetes Mellitus are limited
so these novel compounds can aid for the better treatment for DM who are taking a good diet and
maintaining a healthy lifestyle and who do not respond to any treatment.The predicted Pharmacophore
for Diabetes Mellitus will help in the identification of novel and more effective drugs with less side effects
and more success rates. This study can be further studied for developing more antidiabetic compounds.
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CITATION OF THIS ARTICLE
F Maryam, H Mukhtar, I Bibi, M Rizwan, S Khan, A Mehmood, A Munir. Ligand Based Pharmacophore Modelling,
Virtual Screening And Molecular Docking Of Novel Compounds Against Diabetes . Bull. Env. Pharmacol. Life Sci., Vol 8
[9] August 2019: 38-48
Maryam et al